WorkDOCUMENT × AI
PageWright
Enterprise document automation that solves the combinatorial explosion. 18 million template variations compressed into 100 instruction files. Built on the architecture proven in regulated industries.
pagewright.ai(coming soon)ROLE
Creator
YEAR
2024-2025
STACK
Next.js · Python · OpenAI
STATUS
Available
THE PROBLEM
Complex documents in regulated industries have a combinatorial explosion problem. Every combination of jurisdiction × document type × stakeholder × special conditions produces different requirements.
Traditional solutions fail predictably: template libraries become unmaintainable at scale, rule engines explode with conflicts, and generic AI can't guarantee compliance. The math makes this obvious.
TRADITIONAL TEMPLATES
Documents × Categories × Regulations
× Stakeholders × Conditions × Formats
200 × 30 × 3 × 10 × 20 × 5
18,000,000
templates required
PAGEWRIGHT MDRS
Base + Domain Overrides
+ Regulatory Layers
+ Conditional Logic
~100
instruction files
180,000× reduction in storage. Hierarchical composition instead of enumeration. O(n) instead of O(n^m).
THE BREAKTHROUGH
MDRS (Markdown Document Retrieval System) solves the explosion through hierarchical composition instead of enumeration. Documents assemble dynamically from layered instructions, each with priority rules.
The critical discovery: AI models edit Markdown without syntax errors (100% success rate vs 70-90% for JSON/YAML). This makes instructions human-readable, AI-editable, and git-versioned.
HIERARCHICAL PROMPT ASSEMBLY
BASE INSTRUCTIONS
Default section behavior
REPORT TYPE
Initial vs Reevaluation
DOMAIN OVERLAYS
Disability, category, etc.
REGULATORY
District/jurisdiction rules
Higher priority wins conflicts. Layers merge additively otherwise. One system, infinite variations.
HOW IT WORKS
Smart Ingestion
AI identifies document types from 200+ categories with 99% accuracy. PDFs, images, Word docs, handwritten notes. Multi-model pipeline routes to optimal extraction strategy.
Section Determination
Automatically determines which of 30+ sections to include, their order (different per jurisdiction), and maps 100+ uploaded files to the correct sections. This is the hardest problem-others don't even attempt it.
Hierarchical Generation
Assembles perfect prompts from layered instructions. Base → Domain → Regulatory → Specific. Resolves conflicts by priority. Generates each section with precisely engineered context.
Unlimited Length
Can generate 8-page summaries or 150+ page comprehensive reports. No context limits. No consistency degradation. Tested to 1000+ pages.
PROVEN RESULTS
Architecture proven on Psych Assessment AI-Level 8/10 complexity, harder than 90% of legal/medical documentation.
95%
LABOR REDUCTION
20 hours → 1 hour
99%
COMPLIANCE
Built-in, not bolted-on
$0.40
TOKEN COST
vs $2,500 traditional
8min
GENERATION
vs 15 hours manual
The QA team burden eliminated entirely. Compliance built-in from the start means no revision cycles. Psychologists review instead of write-95% reduction in cognitive load on the most dreaded part of their job.
TARGET MARKETS
PageWright excels where documents are too complex for simple AI, too variable for templates, and too important to get wrong.
TARGET VERTICALS (LEVEL 6-8 COMPLEXITY)
HEALTHCARE
Prior auth, clinical assessments, discharge summaries
LEGAL
Regulatory filings, compliance reports, case summaries
FINANCIAL
Loan origination, audit reports, risk assessments
INSURANCE
Claims processing, policy docs, investigation reports
The sweet spot: Too complex for simple AI. Too variable for templates. Too important to get wrong.
WHY THIS MATTERS
The hard part isn't the AI model - it's the information architecture. How do you structure content for infinite variability while still enabling AI-human collaborative editing?
MDRS reduces complexity from O(n^m) to O(n). That's the difference between "theoretically possible" and "actually works in production." The same architecture that handles psychological assessments can handle insurance claims, legal contracts, or medical documentation.
The implementation isn't trivial - hierarchical composition, AI-editable structures, and validation at scale all took time to figure out. But the pattern is proven and generalizes well.